Analysis of the stance phase of the gait cycle in Parkinson's disease and its potency for Parkinson's disease discrimination

Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier

Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.

Vertical locomotion improves horizontal locomotion: effects of climbing on gait and other mobility aspects in Parkinson's disease. A secondary analysis from a randomized controlled trial

BACKGROUND

In the Climb Up! Head Up! trial, we showed that sport climbing reduces bradykinesia, tremor, and rigidity in mildly to moderately affected participants with Parkinson's disease. This secondary analysis aimed to evaluate the effects of sport climbing on gait and functional mobility in this cohort.

METHODS

Climb Up! Head Up! was a 1:1 randomized controlled trial. Forty-eight PD participants (Hoehn and Yahr stage 2-3) either participated in a 12-week, 90-min-per-week sport climbing course (intervention group) or were engaged in regular unsupervised physical activity (control group). Relevant outcome measures for this analysis were extracted from six inertial measurement units placed on the extremities, chest, and lower back, that were worn during supervised gait and functional mobility assessments before and after the intervention. Assessments included normal and fast walking, dual-tasking walking, Timed Up and Go test, Instrumented Stand and Walk test, and Five Times Sit to Stand test.

RESULTS

Compared to baseline, climbing improved gait speed during normal walking by 0.09 m/s (p = 0.005) and during fast walking by 0.1 m/s. Climbing also reduced the time spent in the stance phase during fast walking by 0.03 s. Climbing improved the walking speed in the 7-m- Timed Up and Go test by 0.1 m/s (p < 0.001) and the turning speed by 0.39 s (p = 0.052), the speed in the Instrumented Stand and Walk test by 0.1 m/s (p < 0.001), and the speed in the Five Times Sit to Stand test by 2.5 s (p = 0.014). There was no effect of sport climbing on gait speed or gait variables during dual-task walking.

CONCLUSIONS

Sport climbing improves gait speed during normal and fast walking, as well as functional mobility in people with Parkinson's disease. Trial registration This study was registered within the U.S. National Library of Medicine (No: NCT04569981, date of registration September 30th, 2020).

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

Laser-light cueing shoes with integrated foot pressure and inertial sensing for investigating the impact of visual cueing on gait characteristics in Parkinson's disease individuals

Gait disorders are a fundamental challenge in Parkinson's disease (PD). The use of laser-light visual cues emitted from shoes has demonstrated effective in improving freezing of gait within less restrictive environments. However, the effectiveness of shoes-based laser-light cueing may vary among individuals with PD who have different types of impairments. We introduced an innovative laser-light visual shoes system capable of producing alternating visual cues for the left and right feet through one-side cueing at a time, while simultaneously recording foot inertial data and foot pressures. The effects of this visual cueing system on gait patterns were assessed in individuals with PD, both those with well-gait and those with worse-gait. Our device successfully quantified gait characteristics, including the asymmetry in the center of pressure trajectory, in individuals with PD. Furthermore, visual cueing prolonged stride times and increased the percentage of stance phase, while concurrently reducing stride length in PD individuals with well-gait. Conversely, in PD individuals with worse-gait, visual cueing resulted in a decreased freeze index and a reduction in the proportion of intervals prone to freezing episodes. The effects of visual cueing varied between PD individuals with well-gait and those with worse-gait. Visual cueing slowed down gait in the well-gait group while it appeared to mitigate freezing episodes in worse-gait group. Future researches, including enhancements to extend the projection distance of visual cues and clinical assessments conducted in real-world settings, will help establish the clinical utility of our proposed visual cueing system.

Vertical Ground Reaction Forces in Parkinson's Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

This study aimed to investigate and compare the vertical Ground Reaction Forces (vGRFs) of patients with Parkinson's Disease (PwPD) and healthy subjects (HS) when the confounding effect of walking speed was absent. Therefore, eighteen PwPD and eighteen age- and linear walking speed-matched HS were recruited. Using plantar pressure insoles, participants walked along linear and curvilinear paths at self-selected speeds. Interestingly, PwPD exhibited similar walking speed to HS during curvilinear trajectories (p = 0.48) and similar vGRF during both linear and curvilinear paths. In both groups, vGRF at initial contact and terminal stance was higher during linear walking, while vGRF at mid-stance was higher in curvilinear trajectories. Similarly, the time to peak vGRF at each phase showed no significant group differences. The vGRF timing variability was different between the two groups, particularly at terminal stance (p < 0.001). In conclusion, PwPD and HS showed similar modifications in vGRF and a similar reduction in gait speed during curvilinear paths when matched for linear walking speed. This emphasized the importance of considering walking speed when assessing gait dynamics in PwPD. This study also suggests the possibility of the variability of specific temporal measures in differentiating the gait patterns of PwPD versus those of HS, even in the early stages of the disease.

Isolating the speed factor is crucial in gait analysis for Parkinson's disease

Introduction

Parkinson's disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants.

Methods

In this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student's t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39).

Results

Statistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p ≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p ≤ 0.05), stride time was similar (p ≥ 0.07).

Discussion

These results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD.

Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases

This paper proposes the transition times of Petri net models of human gait as training features for multiclass random forests (RFs) and classification trees (CTs). These models are designed to support screening for neurodegenerative diseases. The proposed Petri net describes gait in terms of nine cyclic phases and the timing of the nine events that mark the transition between phases. Since the transition times between strides vary, each is represented as a random variable characterized by its mean and standard deviation. These transition times are calculated using the PhysioNet database of vertical ground reaction forces (VGRFs) generated by feet-ground contact. This database comprises the VGRFs of four groups: amyotrophic lateral sclerosis, the control group, Huntington's disease, and Parkinson disease. The RF produced an overall classification accuracy of 91%, and the specificities and sensitivities for each class were between 80% and 100%. However, despite this high performance, the RF-generated models demonstrated lack of interpretability prompted the training of a CT using identical features. The obtained tree comprised only four features and required a maximum of three comparisons. However, this simplification dramatically reduced the overall accuracy from 90.6% to 62.3%. The proposed set features were compared with those included in PhysioNet database of VGRFs. In terms of both the RF and CT, more accurate models were established using our features than those of the PhysioNet.